基于v-Informer的云平台资源负载预测方法
Load Prediction Method of Cloud Resource Based on v-Informer
尤文龙 1邓莉 1李锐龙 1谢雨欣 1任正伟1
作者信息
- 1. 武汉科技大学计算机科学与技术学院 武汉 430065;智能信息处理与实时工业系统湖北省重点实验室 武汉 430065
- 折叠
摘要
目前,云计算技术的使用非常广泛.随着用户量的增加,云计算资源的分配管理也越来越重要,而准确的负载预测是分配管理的重要依据.但由于云平台任务有多个负载特征,且特征的相关性变化趋势各不相同,因此难以从长期的历史数据中提取出有效的依赖信息.在Informer模型的基础上,提出了一种针对高动态云平台任务CPU长期负载预测方法v-Informer,该方法通过变分模态分解来分解负载序列中的变化趋势,引入多头自注意力机制捕获其中的长期依赖性和局部非线性关系,同时应用梯度集中技术改进优化器,减少计算开销.分别在微软云平台和谷歌云平台数据上进行实验,结果表明,与目前已有的CPU负载预测模型LSTM,Transformer,TCN和CEEMDAN-Informer相比,v-Informer在Google数据集上的预测误差分别减少了 34％,19％,15％和6.5％;在微软数据集上的预测误差分别减少了 32％,16％,12％和7％,具有较好的预测精度.
Abstract
Cloud computing technology is widely used at present.With the increase in the number of users,the allocation and management of cloud computing resources is becoming more and more important,and accurate load prediction is an important ba-sis for allocation and management.Based on the Informer model,this paper proposes a long-term CPU load prediction method for high dynamic cloud platform tasks,called v-Informer.v-Informer decomposes the variation trend in the load sequence through va-riational mode decomposition,and introduces a multi-head self-attention mechanism to capture the long-term dependence and local nonlinear relationship.At the same time,the gradient concentration technique is used to improve the optimizer and reduce the computational cost.Experiments are carried out on the data of Microsoft and Google cloud platforms.The results show that,com-pared with the existing CPU load prediction models LSTM,Transformer,TCN and CEEMDAN-Informer,the prediction error of v-Informer is reduced by 34％,19％,15％and 6.5％respectively on the Google dataset.The prediction error on the Microsoft dataset is reduced by 32％,16％,12％and 7％respectively,with better prediction accuracy.
关键词
云平台/CPU负载/多步预测/模态分解/Informer/梯度收敛Key words
Cloud platform/CPU load/Multi-step forecasting/Modal decomposition/Informer/Gradient convergence引用本文复制引用
出版年
2024
NETL
NSTL
万方数据